{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1ztegmwm4sp",
   "metadata": {},
   "source": [
    "## LlamaStack + LangChain Integration Tutorial\n",
    "\n",
    "This notebook demonstrates how to integrate **LlamaStack** with **LangChain** to build a complete RAG (Retrieval-Augmented Generation) system.\n",
    "\n",
    "### Overview\n",
    "\n",
    "- **LlamaStack**: Provides the infrastructure for running LLMs and Open AI Compatible Vector Stores\n",
    "- **LangChain**: Provides the framework for chaining operations and prompt templates\n",
    "- **Integration**: Uses LlamaStack's OpenAI-compatible API with LangChain\n",
    "\n",
    "### What You'll See\n",
    "\n",
    "1. Setting up LlamaStack server with Fireworks AI provider\n",
    "2. Creating and Querying Vector Stores\n",
    "3. Building RAG chains with LangChain + LLAMAStack\n",
    "4. Querying the chain for relevant information\n",
    "\n",
    "### Prerequisites\n",
    "\n",
    "- Fireworks API key\n",
    "\n",
    "---\n",
    "\n",
    "### 1. Installation and Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ktr5ls2cas",
   "metadata": {},
   "source": [
    "#### Install Required Dependencies\n",
    "\n",
    "First, we install all the necessary packages for LangChain and FastAPI integration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5b6a6a17-b931-4bea-8273-0d6e5563637a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: uv in /Users/swapna942/miniconda3/lib/python3.12/site-packages (0.7.20)\n",
      "\u001b[2mUsing Python 3.12.11 environment at: /Users/swapna942/miniconda3\u001b[0m\n",
      "\u001b[2mAudited \u001b[1m7 packages\u001b[0m \u001b[2min 42ms\u001b[0m\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install uv\n",
    "!uv pip install fastapi uvicorn \"langchain>=0.2\" langchain-openai \\\n",
    "             langchain-community langchain-text-splitters \\\n",
    "             faiss-cpu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wmt9jvqzh7n",
   "metadata": {},
   "source": [
    "### 2. LlamaStack Server Setup\n",
    "\n",
    "#### Build and Start LlamaStack Server\n",
    "\n",
    "This section sets up the LlamaStack server with:\n",
    "- **Fireworks AI** as the inference provider\n",
    "- **Sentence Transformers** for embeddings\n",
    "\n",
    "The server runs on `localhost:8321` and provides OpenAI-compatible endpoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd2dacf3-ec8b-4cc7-8ff4-b5b6ea4a6e9e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import subprocess\n",
    "import time\n",
    "\n",
    "# Remove UV_SYSTEM_PYTHON to ensure uv creates a proper virtual environment\n",
    "# instead of trying to use system Python globally, which could cause permission issues\n",
    "# and package conflicts with the system's Python installation\n",
    "if \"UV_SYSTEM_PYTHON\" in os.environ:\n",
    "    del os.environ[\"UV_SYSTEM_PYTHON\"]\n",
    "\n",
    "def run_llama_stack_server_background():\n",
    "    \"\"\"Build and run LlamaStack server in one step using --run flag\"\"\"\n",
    "    log_file = open(\"llama_stack_server.log\", \"w\")\n",
    "    process = subprocess.Popen(\n",
    "        \"uv run --with llama-stack llama stack list-deps starter | xargs -L1 uv pip install\",\n",
    "        \"uv run --with llama-stack llama stack run starter\",\n",
    "        shell=True,\n",
    "        stdout=log_file,\n",
    "        stderr=log_file,\n",
    "        text=True,\n",
    "    )\n",
    "\n",
    "    print(f\"Building and starting Llama Stack server with PID: {process.pid}\")\n",
    "    return process\n",
    "\n",
    "\n",
    "def wait_for_server_to_start():\n",
    "    import requests\n",
    "    from requests.exceptions import ConnectionError\n",
    "\n",
    "    url = \"http://0.0.0.0:8321/v1/health\"\n",
    "    max_retries = 30\n",
    "    retry_interval = 1\n",
    "\n",
    "    print(\"Waiting for server to start\", end=\"\")\n",
    "    for _ in range(max_retries):\n",
    "        try:\n",
    "            response = requests.get(url)\n",
    "            if response.status_code == 200:\n",
    "                print(\"\\nServer is ready!\")\n",
    "                return True\n",
    "        except ConnectionError:\n",
    "            print(\".\", end=\"\", flush=True)\n",
    "            time.sleep(retry_interval)\n",
    "\n",
    "    print(\"\\nServer failed to start after\", max_retries * retry_interval, \"seconds\")\n",
    "    return False\n",
    "\n",
    "\n",
    "def kill_llama_stack_server():\n",
    "    # Kill any existing llama stack server processes using pkill command\n",
    "    os.system(\"pkill -f llama_stack.core.server.server\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "28bd8dbd-4576-4e76-813f-21ab94db44a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building and starting Llama Stack server with PID: 19747\n",
      "Waiting for server to start....\n",
      "Server is ready!\n"
     ]
    }
   ],
   "source": [
    "server_process = run_llama_stack_server_background()\n",
    "assert wait_for_server_to_start()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gr9cdcg4r7n",
   "metadata": {},
   "source": [
    "#### Install LlamaStack Client\n",
    "\n",
    "Install the client library to interact with the LlamaStack server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "487d2dbc-d071-400e-b4f0-dcee58f8dc95",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2mUsing Python 3.12.11 environment at: /Users/swapna942/miniconda3\u001b[0m\n",
      "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 27ms\u001b[0m\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!uv pip install llama_stack_client"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0j5hag7l9x89",
   "metadata": {},
   "source": [
    "### 3. Initialize LlamaStack Client\n",
    "\n",
    "Create a client connection to the LlamaStack server with API keys for different providers:\n",
    "\n",
    "- **Fireworks API Key**: For Fireworks models\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ab4eff97-4565-4c73-b1b3-0020a4c7e2a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_stack_client import LlamaStackClient\n",
    "\n",
    "client = LlamaStackClient(\n",
    "    base_url=\"http://0.0.0.0:8321\",\n",
    "    provider_data={\"fireworks_api_key\": \"***\"},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vwhexjy1e8o",
   "metadata": {},
   "source": [
    "#### Explore Available Models and Safety Features\n",
    "\n",
    "Check what models and safety shields are available through your LlamaStack instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "880443ef-ac3c-48b1-a80a-7dab5b25ac61",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: GET http://0.0.0.0:8321/v1/models \"HTTP/1.1 200 OK\"\n",
      "INFO:httpx:HTTP Request: GET http://0.0.0.0:8321/v1/shields \"HTTP/1.1 200 OK\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available Fireworks models:\n",
      "- fireworks/accounts/fireworks/models/llama-v3p1-8b-instruct\n",
      "- fireworks/accounts/fireworks/models/llama-v3p1-70b-instruct\n",
      "- fireworks/accounts/fireworks/models/llama-v3p1-405b-instruct\n",
      "- fireworks/accounts/fireworks/models/llama-v3p2-3b-instruct\n",
      "- fireworks/accounts/fireworks/models/llama-v3p2-11b-vision-instruct\n",
      "- fireworks/accounts/fireworks/models/llama-v3p2-90b-vision-instruct\n",
      "- fireworks/accounts/fireworks/models/llama-v3p3-70b-instruct\n",
      "- fireworks/accounts/fireworks/models/llama4-scout-instruct-basic\n",
      "- fireworks/accounts/fireworks/models/llama4-maverick-instruct-basic\n",
      "- fireworks/nomic-ai/nomic-embed-text-v1.5\n",
      "- fireworks/accounts/fireworks/models/llama-guard-3-8b\n",
      "- fireworks/accounts/fireworks/models/llama-guard-3-11b-vision\n",
      "----\n",
      "Available shields (safety models):\n",
      "code-scanner\n",
      "llama-guard\n",
      "nemo-guardrail\n",
      "----\n"
     ]
    }
   ],
   "source": [
    "print(\"Available Fireworks models:\")\n",
    "for m in client.models.list():\n",
    "    if m.identifier.startswith(\"fireworks/\"):\n",
    "        print(f\"- {m.identifier}\")\n",
    "\n",
    "print(\"----\")\n",
    "print(\"Available shields (safety models):\")\n",
    "for s in client.shields.list():\n",
    "    print(s.identifier)\n",
    "print(\"----\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gojp7at31ht",
   "metadata": {},
   "source": [
    "### 4. Vector Store Setup\n",
    "\n",
    "#### Create a Vector Store with File Upload\n",
    "\n",
    "Create a vector store using the OpenAI-compatible vector stores API:\n",
    "\n",
    "- **Vector Store**: OpenAI-compatible vector store for document storage\n",
    "- **File Upload**: Automatic chunking and embedding of uploaded files  \n",
    "- **Embedding Model**: Sentence Transformers model for text embeddings\n",
    "- **Dimensions**: 384-dimensional embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be2c2899-ea53-4e5f-b6b8-ed425f5d6572",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/files \"HTTP/1.1 200 OK\"\n",
      "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/files \"HTTP/1.1 200 OK\"\n",
      "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/files \"HTTP/1.1 200 OK\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File(id='file-54652c95c56c4c34918a97d7ff8a4320', bytes=41, created_at=1757442621, expires_at=1788978621, filename='shipping_policy.txt', object='file', purpose='assistants')\n",
      "File(id='file-fb1227c1d1854da1bd774d21e5b7e41c', bytes=48, created_at=1757442621, expires_at=1788978621, filename='returns_policy.txt', object='file', purpose='assistants')\n",
      "File(id='file-673f874852fe42798675a13d06a256e2', bytes=45, created_at=1757442621, expires_at=1788978621, filename='support.txt', object='file', purpose='assistants')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/vector_stores \"HTTP/1.1 200 OK\"\n"
     ]
    }
   ],
   "source": [
    "from io import BytesIO\n",
    "\n",
    "docs = [\n",
    "    (\"Acme ships globally in 3-5 business days.\", {\"title\": \"Shipping Policy\"}),\n",
    "    (\"Returns are accepted within 30 days of purchase.\", {\"title\": \"Returns Policy\"}),\n",
    "    (\"Support is available 24/7 via chat and email.\", {\"title\": \"Support\"}),\n",
    "]\n",
    "\n",
    "file_ids = []\n",
    "for content, metadata in docs:\n",
    "  with BytesIO(content.encode()) as file_buffer:\n",
    "      file_buffer.name = f\"{metadata['title'].replace(' ', '_').lower()}.txt\"\n",
    "      create_file_response = client.files.create(file=file_buffer, purpose=\"assistants\")\n",
    "      print(create_file_response)\n",
    "      file_ids.append(create_file_response.id)\n",
    "\n",
    "# Create vector store with files\n",
    "vector_store = client.vector_stores.create(\n",
    "  name=\"acme_docs\",\n",
    "  file_ids=file_ids,\n",
    "  embedding_model=\"sentence-transformers/all-MiniLM-L6-v2\",\n",
    "  embedding_dimension=384,\n",
    "  provider_id=\"faiss\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9061tmi1zpq",
   "metadata": {},
   "source": [
    "#### Test Vector Store Search\n",
    "\n",
    "Query the vector store. This performs semantic search to find relevant documents based on the query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ba9d1901-bd5e-4216-b3e6-19dc74551cc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/vector_stores/vs_708c060b-45da-423e-8354-68529b4fd1a6/search \"HTTP/1.1 200 OK\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acme ships globally in 3-5 business days.\n",
      "Returns are accepted within 30 days of purchase.\n"
     ]
    }
   ],
   "source": [
    "search_response = client.vector_stores.search(\n",
    "  vector_store_id=vector_store.id,\n",
    "  query=\"How long does shipping take?\",\n",
    "  max_num_results=2\n",
    ")\n",
    "for result in search_response.data:\n",
    "  content = result.content[0].text\n",
    "  print(content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "usne6mbspms",
   "metadata": {},
   "source": [
    "### 5. LangChain Integration\n",
    "\n",
    "#### Configure LangChain with LlamaStack\n",
    "\n",
    "Set up LangChain to use LlamaStack's OpenAI-compatible API:\n",
    "\n",
    "- **Base URL**: Points to LlamaStack's OpenAI endpoint\n",
    "- **Headers**: Include Fireworks API key for model access\n",
    "- **Model**: Use Meta Llama v3p1 8b instruct model for inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c378bd10-09c2-417c-bdfc-1e0a2dd19084",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "# Point LangChain to Llamastack Server\n",
    "llm = ChatOpenAI(\n",
    "    base_url=\"http://0.0.0.0:8321/v1/openai/v1\",\n",
    "    api_key=\"dummy\",\n",
    "    model=\"fireworks/accounts/fireworks/models/llama-v3p1-8b-instruct\",\n",
    "    default_headers={\"X-LlamaStack-Provider-Data\": '{\"fireworks_api_key\": \"***\"}'},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a4ddpcuk3l",
   "metadata": {},
   "source": [
    "#### Test LLM Connection\n",
    "\n",
    "Verify that LangChain can successfully communicate with the LlamaStack server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f88ffb5a-657b-4916-9375-c6ddc156c25e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AIMessage(content=\"A llama's gentle eyes shine bright,\\nIn the Andes, it roams through morning light.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': None, 'model_name': 'fireworks/accounts/fireworks/models/llama-v3p1-8b-instruct', 'system_fingerprint': None, 'id': 'chatcmpl-602b5967-82a3-476b-9cd2-7d3b29b76ee8', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--0933c465-ff4d-4a7b-b7fb-fd97dd8244f3-0')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test llm with simple message\n",
    "messages = [\n",
    "    {\"role\": \"system\", \"content\": \"You are a friendly assistant.\"},\n",
    "    {\"role\": \"user\", \"content\": \"Write a two-sentence poem about llama.\"},\n",
    "]\n",
    "llm.invoke(messages)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0xh0jg6a0l4a",
   "metadata": {},
   "source": [
    "### 6. Building the RAG Chain\n",
    "\n",
    "#### Create a Complete RAG Pipeline\n",
    "\n",
    "Build a LangChain pipeline that combines:\n",
    "\n",
    "1. **Vector Search**: Query LlamaStack's Open AI compatible Vector Store\n",
    "2. **Context Assembly**: Format retrieved documents\n",
    "3. **Prompt Template**: Structure the input for the LLM\n",
    "4. **LLM Generation**: Generate answers using context\n",
    "5. **Output Parsing**: Extract the final response\n",
    "\n",
    "**Chain Flow**: `Query → Vector Search → Context + Question → LLM → Response`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9684427d-dcc7-4544-9af5-8b110d014c42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# LangChain for prompt template and chaining + LLAMA Stack Client Vector DB and LLM chat completion\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
    "\n",
    "\n",
    "def join_docs(docs):\n",
    "    return \"\\n\\n\".join([f\"[{d.filename}] {d.content[0].text}\" for d in docs.data])\n",
    "\n",
    "PROMPT = ChatPromptTemplate.from_messages(\n",
    "    [\n",
    "        (\"system\", \"You are a helpful assistant. Use the following context to answer.\"),\n",
    "        (\"user\", \"Question: {question}\\n\\nContext:\\n{context}\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "vector_step = RunnableLambda(\n",
    "      lambda x: client.vector_stores.search(\n",
    "          vector_store_id=vector_store.id,\n",
    "          query=x,\n",
    "          max_num_results=2\n",
    "      )\n",
    "  )\n",
    "\n",
    "chain = (\n",
    "    {\"context\": vector_step | RunnableLambda(join_docs), \"question\": RunnablePassthrough()}\n",
    "    | PROMPT\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0onu6rhphlra",
   "metadata": {},
   "source": [
    "### 7. Testing the RAG System\n",
    "\n",
    "#### Example 1: Shipping Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "03322188-9509-446a-a4a8-ce3bb83ec87c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/vector_stores/vs_708c060b-45da-423e-8354-68529b4fd1a6/search \"HTTP/1.1 200 OK\"\n",
      "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "❓ How long does shipping take?\n",
      "💡 Acme ships globally in 3-5 business days. This means that shipping typically takes between 3 to 5 working days from the date of dispatch or order fulfillment.\n"
     ]
    }
   ],
   "source": [
    "query = \"How long does shipping take?\"\n",
    "response = chain.invoke(query)\n",
    "print(\"❓\", query)\n",
    "print(\"💡\", response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7krhqj88ku",
   "metadata": {},
   "source": [
    "#### Example 2: Returns Policy Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "61995550-bb0b-46a8-a5d0-023207475d60",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/vector_stores/vs_708c060b-45da-423e-8354-68529b4fd1a6/search \"HTTP/1.1 200 OK\"\n",
      "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "❓ Can I return a product after 40 days?\n",
      "💡 Based on the provided context, you cannot return a product after 40 days. The return window is limited to 30 days from the date of purchase.\n"
     ]
    }
   ],
   "source": [
    "query = \"Can I return a product after 40 days?\"\n",
    "response = chain.invoke(query)\n",
    "print(\"❓\", query)\n",
    "print(\"💡\", response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "h4w24fadvjs",
   "metadata": {},
   "source": [
    "---\n",
    "We have successfully built a RAG system that combines:\n",
    "\n",
    "- **LlamaStack** for infrastructure (LLM serving + Vector Store)\n",
    "- **LangChain** for orchestration (prompts + chains)\n",
    "- **Fireworks** for high-quality language models\n",
    "\n",
    "### Key Benefits\n",
    "\n",
    "1. **Unified Infrastructure**: Single server for LLMs and Vector Store\n",
    "2. **OpenAI Compatibility**: Easy integration with existing LangChain code\n",
    "3. **Multi-Provider Support**: Switch between different LLM providers\n",
    "4. **Production Ready**: Built-in safety shields and monitoring\n",
    "\n",
    "### Next Steps\n",
    "\n",
    "- Add more sophisticated document processing\n",
    "- Implement conversation memory\n",
    "- Add safety filtering and monitoring\n",
    "- Scale to larger document collections\n",
    "- Integrate with web frameworks like FastAPI or Streamlit\n",
    "\n",
    "---\n",
    "\n",
    "##### 🔧 Cleanup\n",
    "\n",
    "Don't forget to stop the LlamaStack server when you're done:\n",
    "\n",
    "```python\n",
    "kill_llama_stack_server()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "15647c46-22ce-4698-af3f-8161329d8e3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "kill_llama_stack_server()"
   ]
  }
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